Method and a machine learning system for classifying objects

ABSTRACT

The present invention describes a method for classifying an object having the following steps:
         receiving at least one item of distance information of an object based on a first electromagnetic signal transmitted by a transmitter device and a first electromagnetic signal received by a receiver device;   receiving at least one item of oscillation information of the object based on a second electromagnetic signal transmitted by a transmitter device and a second electromagnetic signal received by a receiver device, which represents a solid oscillation of at least one subsection of the object;   classifying the object based on the received information.

RELATED APPLICATION INFORMATION

The present application claims priority to and the benefit of Germanpatent application no. 10 2017 217 844.2, which was filed in Germany onOct. 6, 2017, the disclosure which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to a method and a machine learning systemfor classifying objects.

BACKGROUND INFORMATION

The publication “Multi-View 3D Object Detection Network for AutonomousDriving” by Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia, Departmentof Electronic Engineering, Tsinghua University, Baidu Inc. (LIDARarXiv:1611.07759v3 [cs.CV] 22 Jun. 2017) describes a method for highly precise3D object detection in scenarios for highly automated driving. For thepurpose of detecting objects, data of a camera and of a lidar are fedinto an object detection network.

The publication “Learning Hierarchical Semantic Segmentations of LIDARData” by David Dohan, Brian Matejek, and Thomas Funkhouser of PrincetonUniversity, Princeton, N.J., USA, discloses a method for semanticsegmentation of data recorded by a lidar.

SUMMARY OF THE INVENTION

The present invention describes a method for classifying an objecthaving the following steps:

-   -   receiving at least one item of distance information of an object        based on a first electromagnetic signal transmitted by a        transmitter device and a first electromagnetic signal received        by a receiver device;    -   receiving at least one item of oscillation information of the        object based on a second electromagnetic signal transmitted by a        transmitter device and a second electromagnetic signal received        by a receiver device, which represents a solid oscillation of at        least one subsection of the object;    -   classifying the object based on the received information.

Classifying is here understood as the application of a classification toan object by selecting a fitting class of a given classification. Thetransmitter and/or receiver devices may either be different devices orthe same transmitter and/or receiver device. It is also possible to usethe same receiver device and different transmitter devices or differentreceiver devices and the same transmitter device.

The distance information may be a distance between the receiver and/ortransmitter device and the object. The distance information, however,may also represent a different distance or a distance from a differentreference. If the transmitter and/or receiver device is located on avehicle roof for example, it is possible to add an additional offset tothe ascertained distance so that it indicates the distance informationof the distance from various vehicle boundaries such as the bumper forexample.

The oscillation information contains information about the solidoscillations performed by the object, which may be caused for example bysources of noise near the object and/or vibrations and/or othermechanical excitations of the object. Since possibly only a subsectionof the object is detected by the electromagnetic radiation, or onlyinformation of a subsection of the object is detectable by the receiverdevice, these items of oscillation information are also only able torepresent solid oscillations of a subsection of the object.

The reception of information is understood as a provision of thisinformation. The information on the one hand may be transmitted from oneunit to another, for example from one control unit to another controlunit, but they may also be passed on within a control unit and be merelyreceived/utilized by another application such as a computer program or amachine learning method or a neural network. For example, it is possiblethat one control unit both ascertains the information as well asreceives this information. In the present document, the information maybe understood both as measured values as well as measured values thathave already been evaluated. It is possible, for example, for an item ofinformation to represent distance information via a time delay between atransmitted and a received signal or information about a phasedifference between transmitted and received radiation. The same appliesto oscillation information.

By evaluating the oscillation information, this invention offers theadvantage of providing an additional characteristic or an additionalproperty of the object, which may be used advantageously to classify theobject. This makes it possible to improve a classification accuracy or aclassification quality of a respective classification method. Suchmethods may be used for example for operating a vehicle, the vehiclebeing operated on the basis of the classification.

In another specific embodiment of the method, the electromagneticsignals are optical signals. The wavelengths of optical signal aregenerally between 400 nm and 1 mm. In particular, these are opticalsignals of wavelengths between 700 nm and 8,000 nm, further inparticular between 800 nm and 1,400 nm and further in particular between1,000 nm and 1,300 nm.

This specific embodiment of the present invention offers the advantageof making it possible to perform a classification using optical signals,for example of a laser. Since human beings also gather informationprimarily with their eyes, this makes it possible to classify relevantobjects.

If wavelengths between 700 nm and 8,000 nm are used, this has theadvantage that these wavelengths are not visible to the human eye. Whenusing these in areas in which human beings may be present, this avoidsendangering and impairing the human beings.

If wavelengths between 800 nm and 1,400 nm are chosen, then it ispossible to ensure a good range of the optical radiation.

The wavelength range between 1,000 nm and 1,300 nm is advantageous sinceoptical radiation in this wavelength range is directly absorbed by thecornea of the human eye and consequently cannot result in damage to theeye. This ensures a safe use of this method in areas in which humanbeings are present.

In another specific embodiment of the method, the receivedelectromagnetic signals are signals of the transmitted electromagneticsignals that are reflected by the object.

In this specific embodiment, an object is irradiated by a transmitterdevice with electromagnetic radiation, and the radiation reflected atleast partially by the object is detected by a receiver device.Consequently, both the distance information as well as the oscillationinformation are based on physical properties of the transmitted andreceived electromagnetic radiation.

This specific embodiment of the present invention offers the advantagethat a detection of the information is possible merely by evaluating theelectromagnetic radiation, which is transmitted by a transmitter unitand received by a receiver unit. No additional information is requiredfrom external sources such as a server and/or additional sensors and/orsources of information.

In another specific embodiment of the method, the first and secondtransmitted signal is the same transmitted signal and the first andsecond received signal is the same received signal.

In this specific embodiment, the transmitter and receiver device is alsothe same device.

This specific embodiment of the present invention offers the advantageof allowing for a classification merely on the basis of a transmittedand a received signal. This makes it possible to detect and evaluatemultiple object features on the basis of one measurement. This makes itpossible to improve a classification quality at the same costs andwithout substantial additional effort.

In another specific embodiment of the method, the distance informationis ascertained based on a propagation time measurement of the firsttransmitted and first received signal and/or a measurement of a phaserelation between the first transmitted and first received signal and/orvia a triangulation method.

This specific embodiment offers the advantage of being able to obtaindistance information very quickly and in an uncomplicated manner via thepropagation time measurement. It is also possible to ascertain thisinformation and determine it possibly even more precisely via ameasurement of the phase relation.

Alternatively or additionally, the distance may also be determined via atriangulation method, in which the transmitter device and the receiverdevice are separated in terms of location. In most cases, a smallseparation of transmitter device and receiver device in terms oflocation may be necessary because they are different devices. Fortriangulation, however, very large separations may also be chosen, forexample greater than 1 m or greater than 5 m or greater than 100 m. Thismakes it possible to measure even very small movements of objects on thebasis of a spatial deflection of the electromagnetic radiation, forexample in accordance with a laser microphone operated viatriangulation.

Such a method may be applied for example when using transmitter andreceiver devices situated in an offset manner, for example in parkingfacilities or in vehicles.

In another specific embodiment of the method, the oscillationinformation is ascertained based on at least one propagation timemeasurement of the second transmitted and second received signal and/ora measurement of a phase relation between the second transmitted andsecond received signal and/or via a triangulation method.

This specific embodiment of the method likewise offers theabove-mentioned advantages. In particular, by measuring a phase relationbetween transmitted and received radiation it is possible to detect verysmall oscillations of the object such that this method is well suitedfor detecting oscillations of solids. A triangulation method is alsosuitable for measuring the oscillation of solids and is used for examplein some laser microphones.

Both for ascertaining the distance information as well as forascertaining the oscillation information, it is possible to use multiplemethods in combination. By ascertaining the information using multipledifferent methods it is therefore possible to create a redundancy.

In another specific embodiment of the present invention, the distanceinformation is ascertained on the basis of a propagation measurement ofthe first electromagnetic signals and the oscillation information isascertained on the basis of a measurement of a phase relation betweenthe second electromagnetic signals.

This specific embodiment of the method offers the advantage of usingmeasuring methods well suited for ascertaining the distance informationand the oscillation information. Since the measuring methods differ,this also creates a kind of redundancy so that the probability that bothitems of information are flawed is reduced.

In another specific embodiment of the method, surroundings are scannedspatially. For this purpose, at least one item of distance informationand one item of oscillation information are respectively received for atleast two different regions of the surroundings and this is followed bya classification, in particular a semantic segmentation, of the scannedsurroundings on the basis of the received information.

This specific embodiment of the method offers the advantage of producingan image of the surroundings in which objects may be represented in aclassified manner. Such methods are essential for automated vehicles orrobots, in particular if these are to move autonomously. In a semanticsegmentation, this method attempts to assign an object to each measuredvalue. If this is not possible, the measured value is markedaccordingly, for example in that it is assigned the value 0. In thismethod it is not necessary to ascertain for each scanned region of thesurroundings both an item of distance information as well as an item ofoscillation information. It suffices if this information is ascertainedat least for respectively two different regions. It is also conceivablethat substantially more distance values are ascertained in the scanningprocess and that additionally an item of oscillation information isascertained only for a certain percentage.

In another specific embodiment of the present invention, the receiveddistance information and the oscillation information are based onmeasured data of a lidar and/or a radar.

This specific embodiment of the present invention offers the advantagethat the method may be realized by already utilized technologies and mayconsequently be implemented in a very cost-effective and timely manner.Specifically the wavelengths of a lidar allow for reliable measurementsof solid oscillations of objects.

For example, a lidar may be used in addition to the distance detectionas a laser microphone so that information additionally obtained in thismanner allows for an improved classification. Instead of detectingobjects merely on the basis of classical methods by evaluating distancevalues and by geometries and movement detected by these, the noise oroscillation information of the corresponding image region isadditionally supplied to a classifier, which is able to obtain a betterclassification result on the basis of the additional information.

Another advantage is that only one sensor is required in order to obtaintwo items of information that are independent of each other.

Moreover, object tracking is simplified since the detection and trackingof objects is improved on the basis of an object-typical oscillationbehavior.

A method as recited in one of the preceding claims, wherein theclassification occurs on the basis of a comparative classificationalgorithm and/or a machine learning system.

The comparative classification algorithm uses comparative methods forclassification so that a data record with comparative data must beprovided. On the basis of a comparison of the information with thecomparative data it is therefore possible to classify objects.

The machine learning system generally does not require comparative dataof that kind. If the machine learning system is based on a neuralnetwork for example, then this network alone may be sufficient to carryout a classification. On the basis of weights of the neural networkadapted in a learning process, it is possible to classify differentobjects reliably, without requiring additional information fromdatabases or the like for this purpose.

This specific embodiment of the method offers the advantage that on thebasis of the information a classification is possible even when theoscillation information for example per se does not seem to provide adetectable added value since its occurrence initially does not seem tofollow an explicable pattern. When detecting and evaluating a largequantity of measured values, however, it may be possible to extractcharacteristic features that by way of the mentioned methods result inan added value and improved classification qualities. Particularly whenusing a machine learning system, such information may be processed verywell and used profitably.

Furthermore, a machine learning system for classification is claimed,which is configured and was trained so as to perform a classification ofan object, based on at least one item of distance information of theobject, which is supplied to the machine learning system and is based ona first electromagnetic signal transmitted by a transmitter device and afirst electromagnetic signal received by a receiver device, and at leastone item of oscillation information of the object, which is supplied tothe machine learning system and is based on a second electromagneticsignal transmitted by a transmitter device and a second electromagneticsignal received by a receiver device, which represents a solidoscillation of at least one subsection of the object.

Different methods may be used to train the machine learning system suchas monitored learning or unmonitored learning. As a machine learningsystem, it is possible to use for example an artificial neural network,a recurrent neural network, a convolutional neural network or a networkbased on back-propagation. The use of an auto-encoder or comparablestructures or algorithms is also conceivable. Combinations of multiplealgorithms or network types are also possible. Generally, it is alsopossible to network multiple networks with one another and to use outputvalues of individual networks as input values in further networks.

Another specific embodiment of the machine learning system has at leastthree neural networks, the at least one item of distance informationbeing input into an input layer of a first neural network, the at leastone item of oscillation information being input into an input layer of asecond neural network and data output by the first and the second neuralnetworks being input into an input layer of a third neural network, inparticular, a classification of objects being performed by the thirdneural network.

This specific embodiment of the present invention offers the advantagethat initially networks evaluate the two different items of informationand that subsequently the already processed information is transmittedto the third neural network. Depending on the utilized neural network,this makes it possible to improve both the performance of the machinelearning system as well as its classification quality.

Furthermore, a machine-readable storage medium is claimed, on which themachine learning system is stored.

Furthermore, a device for classification, in particular a control unitis claimed, which is configured to implement a method for classificationaccording to a method on which this invention is based.

In another specific embodiment of the present invention, this devicecomprises the machine-readable storage medium disclosed above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic method diagram.

FIG. 2 shows a possible implementation of the method using a machinelearning method.

FIG. 3 shows an exemplary implementation of the method using anothermachine learning method.

DETAILED DESCRIPTION

In a first exemplary embodiment, a laser, which radiates light at awavelength of approximately 900 nm, is mounted as a transmitter devicein a parking facility. The laser is operated in a pulsed manner andtransmits pulses at a length of 5 ns. A highly sensitive photodiode,which is accommodated together with the laser in a common housing andwhich acts as a receiver device, receives electromagnetic signalstransmitted by the laser and reflected by an object. The laser and thephotodiode are operated in time-synchronized manner so as to make itpossible to measure both the propagation time of the laser pulse, whichis required for the laser to travel the distance from the laser to anobject and back to the image sensor, as well as a phase differencebetween the transmitted laser pulse and the received signal.

On a processing unit likewise accommodated in the housing, thetransmitted and received signals are evaluated in such a way that adistance of an object irradiated by the laser is ascertained on thebasis of a propagation time measurement of the transmitted and thereceived signal. In addition, oscillation information of the object isascertained based on a measurement of a phase relation between thetransmitted and the received signal. Since the reflected signal,depending on the distance of the object from the laser, is possiblyreceived only after the laser pulse has been transmitted completely, dueto the short laser pulses, the phase of the transmitted signal that istheoretically available at an infinite pulse length is used forascertaining the phase difference.

The distance and oscillation information ascertained in this manner isprovided by the processing unit and is transmitted via an interface to acentral server.

The method shown in FIG. 1 runs on this central server beginning withstep 101.

In step 102, the distance information is received by the central server.

In step 103, the oscillation information is received by the centralserver.

In step 104, the object is classified based on the received information,that is, it is ascertained what kind of object it is. The object may beassigned to the most varied classes such as for example a motor vehicle,pedestrian, cyclist, shopping cart, etc. Substantially more detailedclassifications may also be performed such as compact automobile,station wagon, limousine, adult person, child, etc.

The method ends with step 105.

In this exemplary embodiment, the classification is performed by amachine learning system, which is sketched in FIG. 2.

Distance information and oscillation information of an object isascertained by a measuring device 201, in this case the laser, thephotodiode and the processing unit. The distance information is fed intoan input layer of a first neural network 202, and the oscillationinformation is fed into a second neural network 203. The two neuralnetworks 202, 203 respectively have multiple hidden layers and an outputlayer. The data read out respectively at the output layers of networks202, 203 are fed as input data into a third neural network 204. Thirdneural network 204 has an output layer having n neurons, n correspondingto the number of different classes that are to be distinguished by theneural network. One class is accordingly associated with each neuron ofthe output layer. In this exemplary embodiment, the value read out ateach neuron of the output layer corresponds to a probability that theobject, whose distance and oscillation information was evaluated, is anobject of the class associated with the respective neuron. In thisexemplary embodiment, the information read out from the output layer ofthird neural network 204 is visually represented on a display 205.

In this exemplary embodiment, there are additionally cameras situated inthe parking facility, which likewise cover the areas that the laser isable to irradiate. These camera images are likewise evaluated and arerepresented on screen 205 together with the additional information fromthe measurements by the laser.

In another exemplary embodiment, the distance and the oscillationinformation is detected by different systems. A radar system covering apredefined area of a parking facility is used to ascertain the distanceinformation. Multiple lasers and laser detectors are used to ascertainthe oscillation information, the laser detectors respectively beingsituated spatially separated from the lasers. In this exemplaryembodiment, the laser and the laser detector are respectively situatedapproximately 10m apart from each other. In this manner it is possibleto detect small solid oscillations of objects using a triangulationmethod. The information detected by the two systems is transmitted to acentral server, which evaluates the information and performs aclassification of the objects detected by the systems based on theinformation.

In another exemplary embodiment, an area of a parking facility isirradiated by multiple lasers, the lasers irradiating areas that arespatially separated from one another. A laser detector is assigned toeach laser, which detects the reflected signals of the respective laser.The lasers are operated in a pulsed manner and are activated intime-delayed manner so that there can be a clear separation of the lightpulses. Based on the signals detected by the laser detectors, aprocessing unit ascertains distance information and oscillationinformation for the different areas or for irradiated objects in thedifferent areas. Both the propagation times of the individual pulsedwave trains as well as the phase differences between the received andthe transmitted pulses are used to ascertain the distance andoscillation information. Since multiple lasers are used in thisexemplary embodiment, it is possible to use the received data togenerate a 3D image of the surroundings representing irradiated objects.In this exemplary embodiment as well, the data are evaluated using aneural network, which performs a classification of the individual itemsof distance and oscillation information. A 3D image of the surroundingsis developed in this manner, in which additionally individual objectsare classified. The image generated in this manner may be subsequentlydisplayed on a screen.

In another exemplary embodiment, a rotatable lidar sensor is mounted ona vehicle. This contains 64 lasers and 64 laser detectors, which areable to cover a vertical visual range of approximately 30°. The sensoris additionally able to rotate up to 20 times per second about avertical axis, which thus allows for a 360° recording of a surroundingsof the vehicle. Depending on the speed of rotation, it is theoreticallypossible to ascertain over 250,000 items of distance and oscillationinformation if a reflection of the transmitted radiation is detectedfrom each irradiated area. In this exemplary embodiment, the distanceand oscillation information is evaluated by a machine learning system,which performs a semantic segmentation of the recorded 360° images. Thiscreates a distance image of a surroundings, which additionally containsinformation about the type of the irradiated objects. Such informationis extremely useful for automated systems, in particular for systemsthat move in automated manner such as vehicles driving in automatedmanner.

In another exemplary embodiment, the distance and oscillationinformation is ascertained by a radar sensor installed in a vehicle.

Here too, both the propagation times as well as the phase differencesbetween transmitted and received radiation are used for ascertainingthis information. The information is subsequently fed into a neuralnetwork, which performs a classification on the basis of the detecteddata.

FIG. 3 shows an exemplary embodiment of a machine learning system, whichis configured and was trained so as to perform a classification of anobject, based on at least one item of distance information of theobject, which is supplied to the machine learning system and is based ona first electromagnetic signal transmitted by a transmitter device and afirst electromagnetic signal received by a receiver device, and at leastone item of oscillation information of the object, which is supplied tothe machine learning system and is based on a second electromagneticsignal transmitted by a transmitter device and a second electromagneticsignal received by a receiver device, which represents a solidoscillation of at least one subsection of the object.

The machine learning system has a first 303 and a second 304convolutional neural network, into which oscillation information 302 anddistance information 301 enter, distance information 301 being fed intofirst neural network 303 and oscillation information 302 being fed intosecond neural network 304. A deconvolutional neural network 306 issituated behind the second neural network, which processes the data ofan output layer of second neural network 304 further. Based on the dataof an output layer of first neural network 303, an interposed algorithm307 performs a first rough image evaluation, the result of which is usedfor pooling the data processed by second neural network 304 anddeconvolutional neural network 306. The pooling is performed in a fourthneural network 308, which at the same time outputs the finalclassification of the fed-in oscillation data 302.

The data output by first neural network 303 are likewise processedfurther in a deconvolutional neural network 305, which outputs aclassification of the fed-in distance data 301. The data output by thedeconvolutional neural networks 308, 305 are fused by a furtheralgorithm 309. An element-wise averaging is performed in the process.

In this exemplary embodiment, data of a vehicle surroundings were fedin, which are representable as semantically segmented 3D surroundings310 on the basis of the data output by algorithm 309, and which may beused for any desired subsequent applications.

In another exemplary embodiment, the machine system is trained in such away that oscillation information 302 is fed into neural network 303 andthe distance information is fed into neural network 304 and aclassification is performed in this manner.

In another exemplary embodiment, both the distance information as wellas the oscillation information is subjected to pooling. For thispurpose, another neural network may be interposed behind thedeconvolutional neural network 305. The pooling performed in thisnetwork is in this exemplary embodiment influenced by output data ofneural network 304. In another exemplary embodiment, pooling occurswithout influence from the respective other evaluation channel (theoscillation information and distance information, respectively).

In another exemplary embodiment, a vehicle is equipped with a lidar anda control unit, which ascertains on the basis of the data detected bythe lidar a semantic segmentation and 3D representation of a vehiclesurroundings. Multiple driver assistance systems are operated based onthe ascertained information of the vehicle information. Inter alia, anemergency braking assistant, an ACC function and an automated parkingsystem are operated using the ascertained information.

In another exemplary embodiment, a lidar is used to scan a surroundingsand both distance information as well as oscillation information isascertained for each scanned area. This information is subsequently fedinto a common neural network, which performs a classification based onthe jointly fed-in information.

In another exemplary embodiment, again a lidar is used to scan asurroundings, multiple spatially separated areas being irradiated by apulsed radiation of a wavelength of 1,300 nm and reflections of theemitted radiation being detected. The measured values and the emissiontimes and phase relations of the transmitted light pulsed aresubsequently fed into a machine learning system, which performs aclassification of the recorded areas on the basis of these data.

This machine learning system is trained using a monitored learningmethod. The above-mentioned data were used as input values for thispurpose and were fed in accordingly. The output values were compared toreference values, which were prepared using a classification algorithmand manual assignment of objects based on camera images of the samesurroundings.

In another exemplary embodiment, a semantic segmentation is performed onthe basis of a lidar sensor, which is mounted on a vehicle, a classbeing assigned to each measurement point recording by the lidar. In thisexemplary embodiment, this would correspond to every distance value thatthe lidar sensor delivers.

The class of vehicle and background are mentioned as class by way ofexample.

The lidar sensor draws a non-tight 360° distance image having 32elevation layers and a rotational speed-dependent azimuth angleresolution. Additionally, the lidar is used to build up a noise map,which represents the noise occurring at each position, of a previouslydefined grid network, since the last frame, or even over several frames.The noise map is prepared on the basis of the solid oscillations.

These two sources of information are treated differently by theclassifier, an AI module in this exemplary embodiment. A spatial contextis used to detect a vehicle on the distance image, i.e. in order to beable to assign a class to an individual distance measurement of thelidar, the classifier considers a predefined number of distancemeasurements that are selected in such a way that they are able todescribe the geometry of the vehicle. In this exemplary embodiment, all32 elevation layers and respectively 32 distance values recorded intime-delayed manner in the 32 elevation layers are used (thiscorresponds to an opening angle of approximately 100°).

In order to be able to detect the vehicle on the basis of the noise map,the classifier simultaneously evaluates only additional measurementpoints (four in each case) bordering respectively one measurement point.

A common AI module generates from the two sources of information thedesired result of the semantic segmentation, which ascertains in thementioned exemplary embodiment a separation of vehicles and background.

ABSTRACT OF THE DISCLOSURE

A method for classifying an object having the following: receiving atleast one item of distance information of an object based on a firstelectromagnetic signal transmitted by a transmitter device and a firstelectromagnetic signal received by a receiver device; receiving at leastone item of oscillation information of the object based on a secondelectromagnetic signal transmitted by a transmitter device and a secondelectromagnetic signal received by a receiver device, which represents asolid oscillation of at least one subsection of the object; andclassifying the object based on the received information.

1-15. (canceled)
 16. A method for classifying an object, the methodcomprising: receiving at least one item of distance information of anobject based on a first electromagnetic signal transmitted by atransmitter device and a first electromagnetic signal received by areceiver device; receiving at least one item of oscillation informationof the object based on a second electromagnetic signal transmitted by atransmitter device and a second electromagnetic signal received by areceiver device, which represents a solid oscillation of at least onesubsection of the object; and classifying the object based on thereceived information.
 17. The method of claim 16, wherein theelectromagnetic signals are optical signals.
 18. The method of claim 16,wherein the received electromagnetic signals are signals of thetransmitted electromagnetic signals that are reflected by the object.19. The method of claim 16, wherein the first and second transmittedsignal is the same transmitted signal and the first and second receivedsignal is the same received signal.
 20. The method of claim 16, whereinthe distance information is ascertained based on a propagation timemeasurement of the first transmitted and first received signal and/or ameasurement of a phase relation between the first transmitted and firstreceived signal and/or via a triangulation method.
 21. The method ofclaim 16, wherein the distance information is ascertained based on atleast one propagation time measurement of the second transmitted andsecond received signal and/or a measurement of a phase relation betweenthe second transmitted and second received signal and/or via atriangulation method.
 22. The method of claim 16, wherein the distanceinformation is ascertained on the basis of a propagation timemeasurement of the first electromagnetic signals and the oscillationinformation is ascertained on the basis of a measurement of a phaserelation between the second electromagnetic signals.
 23. The method ofclaim 16, wherein a spatial scan of a surroundings is performed, atleast one item of distance information and one item of oscillationinformation being respectively received for at least two different areasof the surroundings and a classification being performed of the scannedsurroundings on the basis of the received information.
 24. The method ofclaim 16, wherein the received distance information and the oscillationinformation are based on measured data of a lidar and/or a radar. 25.The method of claim 16, wherein the classification occurs on the basisof a comparison-based classification algorithm and/or a machine learningsystem.
 26. A machine learning system for classification, comprising: amachine learning device which is configured and trained so as to performa classification of the object; wherein the classification is based onat least one item of distance information of an object based on a firstelectromagnetic signal transmitted by a transmitter device and a firstelectromagnetic signal received by a receiver device, which is suppliedto the machine learning device, and at least one item of oscillationinformation of the object based on a second electromagnetic signaltransmitted by a transmitter device and a second electromagnetic signalreceived by a receiver device, which is supplied to the machine learningdevice and which represents a solid oscillation of at least onesubsection of the object.
 27. The machine learning system of claim 26,wherein the machine learning device has at least three neural networks,the at least one item of distance information being input into an inputlayer of a first neural network, the at least one item of oscillationinformation being input into an input layer of a second neural networkand data output by the first and the second neural networks being inputinto an input layer of a third neural network.
 28. A computer readablemedium having program code, which is executable by a processor,comprising: a program code arrangement having program code forclassifying an object, by performing the following: receiving at leastone item of distance information of an object based on a firstelectromagnetic signal transmitted by a transmitter device and a firstelectromagnetic signal received by a receiver device; receiving at leastone item of oscillation information of the object based on a secondelectromagnetic signal transmitted by a transmitter device and a secondelectromagnetic signal received by a receiver device, which represents asolid oscillation of at least one subsection of the object; andclassifying the object based on the received information.
 29. A devicefor classification, comprising: a computer readable medium havingprogram code, which is executable by a processor, including a programcode arrangement having program code for classifying an object, byperforming the following: receiving at least one item of distanceinformation of an object based on a first electromagnetic signaltransmitted by a transmitter device and a first electromagnetic signalreceived by a receiver device; receiving at least one item ofoscillation information of the object based on a second electromagneticsignal transmitted by a transmitter device and a second electromagneticsignal received by a receiver device, which represents a solidoscillation of at least one subsection of the object; and classifyingthe object based on the received information.
 30. The device of claim29, wherein the electromagnetic signals are optical signals.
 31. Themethod of claim 16, wherein the electromagnetic signals are opticalsignals, in particular of wavelengths between 700 nm and 8,000 nm. 32.The method of claim 16, wherein the electromagnetic signals are opticalsignals, in particular of wavelengths between 800 nm and 1,400 nm. 33.The method of claim 16, wherein the electromagnetic signals are opticalsignals, in particular of wavelengths between 1,000 nm and 1,300 nm. 34.The method of claim 16, wherein a spatial scan of a surroundings isperformed, at least one item of distance information and one item ofoscillation information being respectively received for at least twodifferent areas of the surroundings and a classification, in particulara semantic segmentation, being performed of the scanned surroundings onthe basis of the received information.
 35. The machine learning systemof claim 26, wherein the machine learning system has at least threeneural networks, the at least one item of distance information beinginput into an input layer of a first neural network, the at least oneitem of oscillation information being input into an input layer of asecond neural network and data output by the first and the second neuralnetworks being input into an input layer of a third neural network, inparticular, a classification of objects being performed by the thirdneural network.